Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials a...Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials accumulate enormous quantities of data with multi-dimensionality and complexity, which might bury critical ‘structure–properties’ rules yet unfortunately not well explored. Machine learning(ML), as a burgeoning approach in materials science, may dig out the hidden structure–properties relationship from materials bigdata, therefore, has recently garnered much attention in materials science. In this review, we try to shortly summarize recent research progress in this field, following the ML paradigm:(i) data acquisition →(ii) feature engineering →(iii) algorithm →(iv) ML model →(v) model evaluation →(vi) application. In section of application, we summarize recent work by following the ‘material science tetrahedron’:(i) structure and composition →(ii) property →(iii) synthesis →(iv) characterization, in order to reveal the quantitative structure–property relationship and provide inverse design countermeasures. In addition, the concurrent challenges encompassing data quality and quantity, model interpretability and generalizability, have also been discussed. This review intends to provide a preliminary overview of ML from basic algorithms to applications.展开更多
Although the efficiency of CH3 NH3 PI3 has been refreshed to 25.2%,stability and toxicity remain the main challenges for its applications.The search for novel solar-cell absorbers that are highly stable,non-toxic,inex...Although the efficiency of CH3 NH3 PI3 has been refreshed to 25.2%,stability and toxicity remain the main challenges for its applications.The search for novel solar-cell absorbers that are highly stable,non-toxic,inexpensive,and highly efficient is now a viable research focus.In this review,we summarize our recent research into the high-throughput screening and materials design of solar-cell absorbers,including single perovskites,double perovskites,and materials beyond Perovskites.BazrS3(single perovskite),Ba2 BiNbS6(double perovskite),HgAl2 Se4(spinel),and IrSb3(skutterudite)were discovered to be potential candidates in terms of their high stabilities,appropriate bandgaps,small carrier effective masses,and strong optical absorption.展开更多
Defect levels in semiconductor band gaps play a crucial role in functionalized semiconductors for practical applications in optoelectronics;however,first-principle defect calculations based on exchange-correlation fun...Defect levels in semiconductor band gaps play a crucial role in functionalized semiconductors for practical applications in optoelectronics;however,first-principle defect calculations based on exchange-correlation functionals,such as local density approximation,grand gradient approximation(GGA),and hybrid functionals,either underestimate band gaps or misplace defect levels.In this study,we revisited iodine defects in CH_(3)NH_(3)PbI_(3) by combining the accuracy of total energy calculations of GGA and single-electron level calculation of the GW method.The combined approach predicted neutral Im_(i) to be unstable and the transition level of Im_(i)(+1/-1)to be close to the valence band maximum.Therefore,Im_(i) may not be as detrimental as previously reported.Moreover,Vm I may be unstable in the-1 charged state but could still be detrimental owing to the deep transition level of Vm I(+1/0).These results could facilitate the further understanding of the intrinsic point defect and defect passivation observed in CH_(3)NH_(3)PbI_(3).展开更多
基金Project support by the National Natural Science Foundation of China(Grant Nos.11674237 and 51602211)the National Key Research and Development Program of China(Grant No.2016YFB0700700)+1 种基金the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD),ChinaChina Post-doctoral Foundation(Grant No.7131705619).
文摘Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials accumulate enormous quantities of data with multi-dimensionality and complexity, which might bury critical ‘structure–properties’ rules yet unfortunately not well explored. Machine learning(ML), as a burgeoning approach in materials science, may dig out the hidden structure–properties relationship from materials bigdata, therefore, has recently garnered much attention in materials science. In this review, we try to shortly summarize recent research progress in this field, following the ML paradigm:(i) data acquisition →(ii) feature engineering →(iii) algorithm →(iv) ML model →(v) model evaluation →(vi) application. In section of application, we summarize recent work by following the ‘material science tetrahedron’:(i) structure and composition →(ii) property →(iii) synthesis →(iv) characterization, in order to reveal the quantitative structure–property relationship and provide inverse design countermeasures. In addition, the concurrent challenges encompassing data quality and quantity, model interpretability and generalizability, have also been discussed. This review intends to provide a preliminary overview of ML from basic algorithms to applications.
基金Project supported by the National Key Research and Development Program of China(Grant No.2016YFB0700700)the National Natural Science Foundation of China(Grant Nos.11674237,11974257,and 51602211)+1 种基金the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD),Chinathe Suzhou Key Laboratory for Advanced Carbon Materials and Wearable Energy Technologies,China。
文摘Although the efficiency of CH3 NH3 PI3 has been refreshed to 25.2%,stability and toxicity remain the main challenges for its applications.The search for novel solar-cell absorbers that are highly stable,non-toxic,inexpensive,and highly efficient is now a viable research focus.In this review,we summarize our recent research into the high-throughput screening and materials design of solar-cell absorbers,including single perovskites,double perovskites,and materials beyond Perovskites.BazrS3(single perovskite),Ba2 BiNbS6(double perovskite),HgAl2 Se4(spinel),and IrSb3(skutterudite)were discovered to be potential candidates in terms of their high stabilities,appropriate bandgaps,small carrier effective masses,and strong optical absorption.
基金Project supported by the National Natural Science Foundation of China (Grant No. 11974257)the Distinguished Young Talent Funding of Jiangsu Province, China (Grant No. BK20200003)the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
文摘Defect levels in semiconductor band gaps play a crucial role in functionalized semiconductors for practical applications in optoelectronics;however,first-principle defect calculations based on exchange-correlation functionals,such as local density approximation,grand gradient approximation(GGA),and hybrid functionals,either underestimate band gaps or misplace defect levels.In this study,we revisited iodine defects in CH_(3)NH_(3)PbI_(3) by combining the accuracy of total energy calculations of GGA and single-electron level calculation of the GW method.The combined approach predicted neutral Im_(i) to be unstable and the transition level of Im_(i)(+1/-1)to be close to the valence band maximum.Therefore,Im_(i) may not be as detrimental as previously reported.Moreover,Vm I may be unstable in the-1 charged state but could still be detrimental owing to the deep transition level of Vm I(+1/0).These results could facilitate the further understanding of the intrinsic point defect and defect passivation observed in CH_(3)NH_(3)PbI_(3).
基金supported by the National Key Research and Development Program of China (2020YFB1506400)the National Natural Science Foundation of China (11974257)Jiangsu Distinguished Young Talent Funding (BK20200003)。
基金Yin WJ acknowledges funding support from the National Key Research and Development Program of China(2016YFB0700700)the National Natural Science Foundation of China(11974257,11674237 and 51602211)+1 种基金the Natural Science Foundation of Jiangsu Province of China(BK20160299)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).The theoretical work was carried out at the National Supercomputer Center in Tianjin and the calculations were performed on TianHe-l(A).
